Abstract
Index is an indispensable part of database. As we enter the era of big data, the traditional index structure is found not to support large-scale data well. Although many index structures such as learned indexes based on machine learning have been proposed to solve such problems of traditional indexes, it is a great challenge to select the most suitable learned indexes for the specific application. To solve this problem, we design ALMSS, an automatic learned index model selection system, which provides a user-friendly interface and can help users automatically select the learned index model. In this paper, we introduce the overall architecture and main technologies of ALMSS, and show the demonstration of this system.
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Zhu, R., Wang, H., Tang, Y., Xu, B. (2021). ALMSS: Automatic Learned Index Model Selection System. In: U, L.H., Spaniol, M., Sakurai, Y., Chen, J. (eds) Web and Big Data. APWeb-WAIM 2021. Lecture Notes in Computer Science(), vol 12859. Springer, Cham. https://doi.org/10.1007/978-3-030-85899-5_34
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DOI: https://doi.org/10.1007/978-3-030-85899-5_34
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